The present invention relates generally to the field of data analysis, and more particularly to modeling based on user biases.
Computing systems are utilized by individuals for a variety of purposes, such as; performing work, communicating, storing information, calculating data, entertainment, and convenience. Therefore, based upon achieving one or more specific purposes within the computing system, users interact with multiple different software applications (e.g., email, Internet browsers, word processors, etc.). While varying purposes may dictate the specific software applications a user utilizes within the computing systems, the manner in which users interact with the computing systems varies based on the individual. Individual users consume and manipulate information supplied by the software applications in different manners based upon preferred learning styles. Learning styles encompass an individuals' natural or habitual pattern of acquiring, processing, and responding to information in learning situations (e.g., visual, verbal, active, sequential, intuitive, etc.).
Regardless of an individual's preferred learning style, users can utilize a common utility, such as a clipboard in conjunction with a clipboard manager, while performing tasks on a computing systems. The clipboard is a set of functions and messages that enables application software to transfer data. As all applications have access to the clipboard, data can be easily transferred between and/or within an application. The clipboard manager is a computer program that adds functionality to the clipboard of an operating system. Clipboard managers enhance the basic functions of cut, copy and paste operations with one or more features such as multiple buffers and the ability to merge, split, and edit contents, selecting the buffer to store data from a cut or copy, selecting the buffer the paste data should be retrieved from, handling formatted text, tabular data, data objects, media content and uniform resource locators (URLs), saving copied data to long term storage, indexing and/or tagging clipped data, and searching saved data.
To determine information regarding users and learning styles, data mining techniques are utilized. The data mining techniques analyze large quantities of data to extract previously unknown patterns utilizing techniques such as cluster analysis, anomaly detection, and association rule mining (i.e., dependencies). The extracted information is then transformed into an understandable structure comprised of patterns and knowledge for further use.